面向光通信的优化型基于SNN的DFE的高效FPGA实现
Efficient FPGA Implementation of an Optimized SNN-based DFE for Optical Communications
AI总结：
本研究针对光通信场景，探索基于脉冲神经网络（SNN）的判决反馈均衡器（DFE）的设计空间以降低计算复杂度，实现了高效FPGA部署，在吞吐量相当的前提下性能优于ANN-based DFE且能效提升25倍。
摘要：
通信系统对更高数据速率的需求持续增长，这使得对具备更优性能的先进非线性均衡器的需求愈发迫切。近年来，人工神经网络（ANNs）被证实是先进非线性均衡器的可行候选方案，其性能优于传统方法，但它们计算复杂度高，因此功耗较大。脉冲神经网络（SNNs）作为ANNs的高能效替代方案开始受到关注，已有研究证明SNNs在该任务上的表现能够超越ANNs。本研究探索了基于SNN的判决反馈均衡器（DFE）的设计空间，以降低其计算复杂度，从而在现场可编程门阵列（FPGA）上实现高效部署。研究结果表明，该方案在吞吐量大致相当的情况下，通信性能优于基于ANN的DFE，且能效提升25倍。
英文摘要：
The ever-increasing demand for higher data rates in communication systems intensifies the need for advanced non-linear equalizers capable of higher performance. Recently artificial neural networks (ANNs) were introduced as a viable candidate for advanced non-linear equalizers, as they outperform traditional methods. However, they are computationally complex and therefore power hungry. Spiking neural networks (SNNs) started to gain attention as an energy-efficient alternative to ANNs. Recent works proved that they can outperform ANNs at this task. In this work, we explore the design space of an SNN-based decision-feedback equalizer (DFE) to reduce its computational complexity for an efficient implementation on field programmable gate array (FPGA). Our Results prove that it achieves higher communication performance than ANN-based DFE at roughly the same throughput and at 25X higher energy efficiency.
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